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Found 25 papers, 9 papers with code
SIR-DIFF: Sparse Image Sets Restoration with Multi-View Diffusion Model
The computer vision community has developed numerous techniques for digitally restoring true scene information from single-view degraded photographs, an important yet extremely ill-posed task.
LIFT-GS: Cross-Scene Render-Supervised Distillation for 3D Language Grounding
Our approach to training 3D vision-language understanding models is to train a feedforward model that makes predictions in 3D, but never requires 3D labels and is supervised only in 2D, using 2D losses and differentiable rendering.
Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models
Recent AI-based 3D content creation has largely evolved along two paths: feed-forward image-to-3D reconstruction approaches and 3D generative models trained with 2D or 3D supervision.
Cocoon: Robust Multi-Modal Perception with Uncertainty-Aware Sensor Fusion
An important paradigm in 3D object detection is the use of multiple modalities to enhance accuracy in both normal and challenging conditions, particularly for long-tail scenarios.
This&That: Language-Gesture Controlled Video Generation for Robot Planning
We propose a robot learning method for communicating, planning, and executing a wide range of tasks, dubbed This&That.
DiffusionPDE: Generative PDE-Solving Under Partial Observation
We introduce a general framework for solving partial differential equations (PDEs) using generative diffusion models.
TC4D: Trajectory-Conditioned Text-to-4D Generation
We learn local deformations that conform to the global trajectory using supervision from a text-to-video model.
FAR: Flexible, Accurate and Robust 6DoF Relative Camera Pose Estimation
Estimating relative camera poses between images has been a central problem in computer vision.
FAR: Flexible Accurate and Robust 6DoF Relative Camera Pose Estimation
Estimating relative camera poses between images has been a central problem in computer vision.
4D-fy: Text-to-4D Generation Using Hybrid Score Distillation Sampling
Recent breakthroughs in text-to-4D generation rely on pre-trained text-to-image and text-to-video models to generate dynamic 3D scenes.
Generative Novel View Synthesis with 3D-Aware Diffusion Models
We present a diffusion-based model for 3D-aware generative novel view synthesis from as few as a single input image.
CurveCloudNet: Processing Point Clouds with 1D Structure
Modern depth sensors such as LiDAR operate by sweeping laser-beams across the scene, resulting in a point cloud with notable 1D curve-like structures.
CC3D: Layout-Conditioned Generation of Compositional 3D Scenes
In this work, we introduce CC3D, a conditional generative model that synthesizes complex 3D scenes conditioned on 2D semantic scene layouts, trained using single-view images.
PartNeRF: Generating Part-Aware Editable 3D Shapes without 3D Supervision
Evaluations on various ShapeNet categories demonstrate the ability of our model to generate editable 3D objects of improved fidelity, compared to previous part-based generative approaches that require 3D supervision or models relying on NeRFs.
LEGO-Net: Learning Regular Rearrangements of Objects in Rooms
Humans universally dislike the task of cleaning up a messy room.
Generating Part-Aware Editable 3D Shapes Without 3D Supervision
Evaluations on various ShapeNet categories demonstrate the ability of our model to generate editable 3D objects of improved fidelity, compared to previous part-based generative approaches that require 3D supervision or models relying on NeRFs.
ALTO: Alternating Latent Topologies for Implicit 3D Reconstruction
One school of thought is to encode a latent vector for each point (point latents).
SinGRAF: Learning a 3D Generative Radiance Field for a Single Scene
Generative models have shown great promise in synthesizing photorealistic 3D objects, but they require large amounts of training data.
3D-Aware Video Generation
Generative models have emerged as an essential building block for many image synthesis and editing tasks.
StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generation
We introduce a high resolution, 3D-consistent image and shape generation technique which we call StyleSDF.
BACON: Band-limited Coordinate Networks for Multiscale Scene Representation
These networks are trained to map continuous input coordinates to the value of a signal at each point.
Seeing the World in a Bag of Chips
We address the dual problems of novel view synthesis and environment reconstruction from hand-held RGBD sensors.
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
In this work, we introduce DeepSDF, a learned continuous Signed Distance Function (SDF) representation of a class of shapes that enables high quality shape representation, interpolation and completion from partial and noisy 3D input data.
Surface Light Field Fusion
We present an approach for interactively scanning highly reflective objects with a commodity RGBD sensor.
Prevalence and recoverability of syntactic parameters in sparse distributed memories
We propose a new method, based on Sparse Distributed Memory (Kanerva Networks), for studying dependency relations between different syntactic parameters in the Principles and Parameters model of Syntax.
